Nishka Kishore

This study proactively evaluates the functional effects of 9790 AIRE missense variants using a reporter assay, providing evidence for 70% of previously classified variants of uncertain significance.

The paper introduces VIS-seq, an image-based method to assess variant effects on cellular phenotypes, revealing multidimensional variant impacts across different diseases.

This study functionally tests STK11 missense variants in mammalian cells, identifying pathogenic variants and providing insights for clinical interpretation.

The paper shows BRCA1 correlates with proliferation in human neural progenitor cells and medulloblastoma, particularly in Group 3 and 4 tumors, and is linked to worse prognosis.

The paper uses a yeast two-hybrid screen to identify novel effector-effector interactions in L. pneumophila, doubling the known number of such interactions.

Pacybara improves the accuracy of long-read sequencing for barcoded mutagenized libraries by addressing sequencing errors and non-unique barcodes.

The paper creates a functional map of all possible missense variants in CHK2 to assess their impact on cancer risk.

This study systematically tests HMBS missense variants to determine their functional impact and distinguish pathogenic from benign variants.

The paper maps binary SARS-CoV-2 protein interactions with host proteins, identifying key processes like immune signaling and ubiquitination, and suggests therapeutic targets.

MaveRegistry is a platform for tracking and sharing multiplexed variant effect assays to reduce redundancy and enable collaboration in variant interpretation.

The paper constructs a high-quality yeast binary interactome map and compares inner- and outer-complexome organization, finding the outer-complexome contains more interactions and exhibits higher functional heterogeneity.

The paper presents HuRI, a comprehensive human protein-protein interaction map, and demonstrates its utility in understanding cellular function and disease mechanisms.

The paper challenges the Triadic Closure Principle in protein interaction prediction and introduces the L3 principle, which outperforms existing methods and identifies novel interactions.